Overview

Dataset statistics

Number of variables50
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory225.2 MiB
Average record size in memory2.3 KiB

Variable types

CAT36
NUM13
BOOL1

Warnings

examide has constant value "101766" Constant
citoglipton has constant value "101766" Constant
medical_specialty has a high cardinality: 73 distinct values High cardinality
diag_1 has a high cardinality: 717 distinct values High cardinality
diag_2 has a high cardinality: 749 distinct values High cardinality
diag_3 has a high cardinality: 790 distinct values High cardinality
number_emergency is highly skewed (γ1 = 22.85558215) Skewed
encounter_id has unique values Unique
num_procedures has 46652 (45.8%) zeros Zeros
number_outpatient has 85027 (83.6%) zeros Zeros
number_emergency has 90383 (88.8%) zeros Zeros
number_inpatient has 67630 (66.5%) zeros Zeros

Reproduction

Analysis started2022-02-02 23:48:01.995007
Analysis finished2022-02-02 23:48:59.380117
Duration57.39 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

encounter_id
Real number (ℝ≥0)

UNIQUE

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165201645.6
Minimum12522
Maximum443867222
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:48:59.500989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median152388987
Q3230270887.5
95-th percentile378962843
Maximum443867222
Range443854700
Interquartile range (IQR)145309693.5

Descriptive statistics

Standard deviation102640296
Coefficient of variation (CV)0.6213031087
Kurtosis-0.1020713932
Mean165201645.6
Median Absolute Deviation (MAD)70921143
Skewness0.6991415513
Sum1.681191067e+13
Variance1.053503036e+16
MonotocityNot monotonic
2022-02-02T15:48:59.632822image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22783921< 0.1%
 
1907920441< 0.1%
 
1907900701< 0.1%
 
1907897221< 0.1%
 
1907868061< 0.1%
 
1907850181< 0.1%
 
1907814121< 0.1%
 
1907758861< 0.1%
 
1907645041< 0.1%
 
1907603221< 0.1%
 
1907449021< 0.1%
 
1907378041< 0.1%
 
1907368021< 0.1%
 
1907257261< 0.1%
 
1907170321< 0.1%
 
1907121361< 0.1%
 
1907100121< 0.1%
 
1907097301< 0.1%
 
1907090221< 0.1%
 
1907076541< 0.1%
 
1907059261< 0.1%
 
1907007721< 0.1%
 
1907000281< 0.1%
 
1907910241< 0.1%
 
1907926081< 0.1%
 
Other values (101741)101741> 99.9%
 
ValueCountFrequency (%) 
125221< 0.1%
 
157381< 0.1%
 
166801< 0.1%
 
282361< 0.1%
 
357541< 0.1%
 
369001< 0.1%
 
409261< 0.1%
 
425701< 0.1%
 
558421< 0.1%
 
622561< 0.1%
 
ValueCountFrequency (%) 
4438672221< 0.1%
 
4438571661< 0.1%
 
4438541481< 0.1%
 
4438477821< 0.1%
 
4438475481< 0.1%
 
4438471761< 0.1%
 
4438427781< 0.1%
 
4438423401< 0.1%
 
4438421361< 0.1%
 
4438420701< 0.1%
 

patient_nbr
Real number (ℝ≥0)

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330400.69
Minimum135
Maximum189502619
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:48:59.792651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.75
Q123413221
median45505143
Q387545949.75
95-th percentile111480273
Maximum189502619
Range189502484
Interquartile range (IQR)64132728.75

Descriptive statistics

Standard deviation38696359.35
Coefficient of variation (CV)0.7122413759
Kurtosis-0.3473720444
Mean54330400.69
Median Absolute Deviation (MAD)32950134
Skewness0.4712807224
Sum5.528987557e+12
Variance1.497408227e+15
MonotocityNot monotonic
2022-02-02T15:48:59.928476image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8878589140< 0.1%
 
4314090628< 0.1%
 
166029323< 0.1%
 
8822754023< 0.1%
 
2319902123< 0.1%
 
2364340522< 0.1%
 
8442861322< 0.1%
 
9270935121< 0.1%
 
8878970720< 0.1%
 
2990387720< 0.1%
 
9060980420< 0.1%
 
8947240220< 0.1%
 
2339848820< 0.1%
 
3709686620< 0.1%
 
8847903619< 0.1%
 
8868195019< 0.1%
 
9739100719< 0.1%
 
9175112118< 0.1%
 
2401157718< 0.1%
 
348127218< 0.1%
 
340105518< 0.1%
 
9116028018< 0.1%
 
8434879218< 0.1%
 
10675747817< 0.1%
 
9048919517< 0.1%
 
Other values (71493)10124599.5%
 
ValueCountFrequency (%) 
1352< 0.1%
 
3781< 0.1%
 
7291< 0.1%
 
7741< 0.1%
 
9271< 0.1%
 
11525< 0.1%
 
13051< 0.1%
 
13143< 0.1%
 
16291< 0.1%
 
20251< 0.1%
 
ValueCountFrequency (%) 
1895026191< 0.1%
 
1894814781< 0.1%
 
1894451271< 0.1%
 
1893658641< 0.1%
 
1893510951< 0.1%
 
1893494301< 0.1%
 
1893320871< 0.1%
 
1892988771< 0.1%
 
1892578462< 0.1%
 
1892157621< 0.1%
 

race
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Caucasian
76099 
AfricanAmerican
19210 
?
 
2273
Hispanic
 
2037
Other
 
1506
ValueCountFrequency (%) 
Caucasian7609974.8%
 
AfricanAmerican1921018.9%
 
?22732.2%
 
Hispanic20372.0%
 
Other15061.5%
 
Asian6410.6%
 
2022-02-02T15:49:00.066315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:00.148220image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:00.251112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length9
Mean length9.849507694
Min length1

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a26939526.9%
 
i11923411.9%
 
n11719711.7%
 
c11655611.6%
 
s787777.9%
 
C760997.6%
 
u760997.6%
 
r399264.0%
 
A390613.9%
 
e207162.1%
 
f192101.9%
 
m192101.9%
 
?22730.2%
 
H20370.2%
 
p20370.2%
 
O15060.2%
 
t15060.2%
 
h15060.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter88136987.9%
 
Uppercase Letter11870311.8%
 
Other Punctuation22730.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C7609964.1%
 
A3906132.9%
 
H20371.7%
 
O15061.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a26939530.6%
 
i11923413.5%
 
n11719713.3%
 
c11655613.2%
 
s787778.9%
 
u760998.6%
 
r399264.5%
 
e207162.4%
 
f192102.2%
 
m192102.2%
 
p20370.2%
 
t15060.2%
 
h15060.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?2273100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin100007299.8%
 
Common22730.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a26939526.9%
 
i11923411.9%
 
n11719711.7%
 
c11655611.7%
 
s787777.9%
 
C760997.6%
 
u760997.6%
 
r399264.0%
 
A390613.9%
 
e207162.1%
 
f192101.9%
 
m192101.9%
 
H20370.2%
 
p20370.2%
 
O15060.2%
 
t15060.2%
 
h15060.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
?2273100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1002345100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a26939526.9%
 
i11923411.9%
 
n11719711.7%
 
c11655611.6%
 
s787777.9%
 
C760997.6%
 
u760997.6%
 
r399264.0%
 
A390613.9%
 
e207162.1%
 
f192101.9%
 
m192101.9%
 
?22730.2%
 
H20370.2%
 
p20370.2%
 
O15060.2%
 
t15060.2%
 
h15060.2%
 

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Female
54708 
Male
47055 
Unknown/Invalid
 
3
ValueCountFrequency (%) 
Female5470853.8%
 
Male4705546.2%
 
Unknown/Invalid3< 0.1%
 
2022-02-02T15:49:00.363984image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:00.436895image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:00.510796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length6
Mean length5.075496728
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e15647130.3%
 
a10176619.7%
 
l10176619.7%
 
F5470810.6%
 
m5470810.6%
 
M470559.1%
 
n12< 0.1%
 
U3< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
/3< 0.1%
 
I3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41474180.3%
 
Uppercase Letter10176919.7%
 
Other Punctuation3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F5470853.8%
 
M4705546.2%
 
U3< 0.1%
 
I3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e15647137.7%
 
a10176624.5%
 
l10176624.5%
 
m5470813.2%
 
n12< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin516510> 99.9%
 
Common3< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e15647130.3%
 
a10176619.7%
 
l10176619.7%
 
F5470810.6%
 
m5470810.6%
 
M470559.1%
 
n12< 0.1%
 
U3< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
I3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/3100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII516513100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e15647130.3%
 
a10176619.7%
 
l10176619.7%
 
F5470810.6%
 
m5470810.6%
 
M470559.1%
 
n12< 0.1%
 
U3< 0.1%
 
k3< 0.1%
 
o3< 0.1%
 
w3< 0.1%
 
/3< 0.1%
 
I3< 0.1%
 
v3< 0.1%
 
i3< 0.1%
 
d3< 0.1%
 

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
[70-80)
26068 
[60-70)
22483 
[50-60)
17256 
[80-90)
17197 
[40-50)
9685 
Other values (5)
9077 
ValueCountFrequency (%) 
[70-80)2606825.6%
 
[60-70)2248322.1%
 
[50-60)1725617.0%
 
[80-90)1719716.9%
 
[40-50)96859.5%
 
[30-40)37753.7%
 
[90-100)27932.7%
 
[20-30)16571.6%
 
[10-20)6910.7%
 
[0-10)1610.2%
 
2022-02-02T15:49:00.614687image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:00.696579image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:00.826439image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length7.025863255
Min length6

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
020632528.9%
 
[10176614.2%
 
-10176614.2%
 
)10176614.2%
 
7485516.8%
 
8432656.1%
 
6397395.6%
 
5269413.8%
 
9199902.8%
 
4134601.9%
 
354320.8%
 
136450.5%
 
223480.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number40969657.3%
 
Open Punctuation10176614.2%
 
Dash Punctuation10176614.2%
 
Close Punctuation10176614.2%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[101766100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
020632550.4%
 
74855111.9%
 
84326510.6%
 
6397399.7%
 
5269416.6%
 
9199904.9%
 
4134603.3%
 
354321.3%
 
136450.9%
 
223480.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-101766100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)101766100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common714994100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
020632528.9%
 
[10176614.2%
 
-10176614.2%
 
)10176614.2%
 
7485516.8%
 
8432656.1%
 
6397395.6%
 
5269413.8%
 
9199902.8%
 
4134601.9%
 
354320.8%
 
136450.5%
 
223480.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII714994100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
020632528.9%
 
[10176614.2%
 
-10176614.2%
 
)10176614.2%
 
7485516.8%
 
8432656.1%
 
6397395.6%
 
5269413.8%
 
9199902.8%
 
4134601.9%
 
354320.8%
 
136450.5%
 
223480.3%
 

weight
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
98569 
[75-100)
 
1336
[50-75)
 
897
[100-125)
 
625
[125-150)
 
145
Other values (5)
 
194
ValueCountFrequency (%) 
?9856996.9%
 
[75-100)13361.3%
 
[50-75)8970.9%
 
[100-125)6250.6%
 
[125-150)1450.1%
 
[25-50)970.1%
 
[0-25)48< 0.1%
 
[150-175)35< 0.1%
 
[175-200)11< 0.1%
 
>2003< 0.1%
 
2022-02-02T15:49:00.938309image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:01.015221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:01.142058image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length1
Mean length1.217096083
Min length1

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories6 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
?9856979.6%
 
051724.2%
 
543683.5%
 
[31942.6%
 
-31942.6%
 
)31942.6%
 
129572.4%
 
722791.8%
 
29290.8%
 
>3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Other Punctuation9856979.6%
 
Decimal Number1570512.7%
 
Open Punctuation31942.6%
 
Dash Punctuation31942.6%
 
Close Punctuation31942.6%
 
Math Symbol3< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?98569100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[3194100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0517232.9%
 
5436827.8%
 
1295718.8%
 
7227914.5%
 
29295.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3194100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3194100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common123859100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
?9856979.6%
 
051724.2%
 
543683.5%
 
[31942.6%
 
-31942.6%
 
)31942.6%
 
129572.4%
 
722791.8%
 
29290.8%
 
>3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII123859100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
?9856979.6%
 
051724.2%
 
543683.5%
 
[31942.6%
 
-31942.6%
 
)31942.6%
 
129572.4%
 
722791.8%
 
29290.8%
 
>3< 0.1%
 

admission_type_id
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024006053
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:01.239944image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.44540283
Coefficient of variation (CV)0.7141296972
Kurtosis1.942476114
Mean2.024006053
Median Absolute Deviation (MAD)0
Skewness1.591984327
Sum205975
Variance2.08918934
MonotocityNot monotonic
2022-02-02T15:49:01.325844image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
15399053.1%
 
31886918.5%
 
21848018.2%
 
652915.2%
 
547854.7%
 
83200.3%
 
721< 0.1%
 
410< 0.1%
 
ValueCountFrequency (%) 
15399053.1%
 
21848018.2%
 
31886918.5%
 
410< 0.1%
 
547854.7%
 
652915.2%
 
721< 0.1%
 
83200.3%
 
ValueCountFrequency (%) 
83200.3%
 
721< 0.1%
 
652915.2%
 
547854.7%
 
410< 0.1%
 
31886918.5%
 
21848018.2%
 
15399053.1%
 

discharge_disposition_id
Real number (ℝ≥0)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.715641766
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:01.429735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.280165509
Coefficient of variation (CV)1.421064204
Kurtosis6.003346764
Mean3.715641766
Median Absolute Deviation (MAD)0
Skewness2.563066993
Sum378126
Variance27.88014781
MonotocityNot monotonic
2022-02-02T15:49:01.541591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
16023459.2%
 
31395413.7%
 
61290212.7%
 
1836913.6%
 
221282.1%
 
2219932.0%
 
1116421.6%
 
511841.2%
 
259891.0%
 
48150.8%
 
76230.6%
 
234120.4%
 
133990.4%
 
143720.4%
 
281390.1%
 
81080.1%
 
15630.1%
 
2448< 0.1%
 
921< 0.1%
 
1714< 0.1%
 
1611< 0.1%
 
198< 0.1%
 
106< 0.1%
 
275< 0.1%
 
123< 0.1%
 
ValueCountFrequency (%) 
16023459.2%
 
221282.1%
 
31395413.7%
 
48150.8%
 
511841.2%
 
61290212.7%
 
76230.6%
 
81080.1%
 
921< 0.1%
 
106< 0.1%
 
ValueCountFrequency (%) 
281390.1%
 
275< 0.1%
 
259891.0%
 
2448< 0.1%
 
234120.4%
 
2219932.0%
 
202< 0.1%
 
198< 0.1%
 
1836913.6%
 
1714< 0.1%
 

admission_source_id
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.754436649
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:01.644471image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.064080834
Coefficient of variation (CV)0.7062517293
Kurtosis1.744989372
Mean5.754436649
Median Absolute Deviation (MAD)0
Skewness1.029934878
Sum585606
Variance16.51675303
MonotocityNot monotonic
2022-02-02T15:49:01.746352image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
75749456.5%
 
12956529.1%
 
1767816.7%
 
431873.1%
 
622642.2%
 
211041.1%
 
58550.8%
 
31870.2%
 
201610.2%
 
91250.1%
 
816< 0.1%
 
2212< 0.1%
 
108< 0.1%
 
142< 0.1%
 
112< 0.1%
 
252< 0.1%
 
131< 0.1%
 
ValueCountFrequency (%) 
12956529.1%
 
211041.1%
 
31870.2%
 
431873.1%
 
58550.8%
 
622642.2%
 
75749456.5%
 
816< 0.1%
 
91250.1%
 
108< 0.1%
 
ValueCountFrequency (%) 
252< 0.1%
 
2212< 0.1%
 
201610.2%
 
1767816.7%
 
142< 0.1%
 
131< 0.1%
 
112< 0.1%
 
108< 0.1%
 
91250.1%
 
816< 0.1%
 

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395986872
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:01.843256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.985107767
Coefficient of variation (CV)0.6790529304
Kurtosis0.8502508405
Mean4.395986872
Median Absolute Deviation (MAD)2
Skewness1.133998719
Sum447362
Variance8.910868383
MonotocityNot monotonic
2022-02-02T15:49:01.944133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
31775617.4%
 
21722416.9%
 
11420814.0%
 
41392413.7%
 
599669.8%
 
675397.4%
 
758595.8%
 
843914.3%
 
930022.9%
 
1023422.3%
 
1118551.8%
 
1214481.4%
 
1312101.2%
 
1410421.0%
 
ValueCountFrequency (%) 
11420814.0%
 
21722416.9%
 
31775617.4%
 
41392413.7%
 
599669.8%
 
675397.4%
 
758595.8%
 
843914.3%
 
930022.9%
 
1023422.3%
 
ValueCountFrequency (%) 
1410421.0%
 
1312101.2%
 
1214481.4%
 
1118551.8%
 
1023422.3%
 
930022.9%
 
843914.3%
 
758595.8%
 
675397.4%
 
599669.8%
 

payer_code
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
40256 
MC
32439 
HM
6274 
SP
5007 
BC
4655 
Other values (13)
13135 
ValueCountFrequency (%) 
?4025639.6%
 
MC3243931.9%
 
HM62746.2%
 
SP50074.9%
 
BC46554.6%
 
MD35323.5%
 
CP25332.5%
 
UN24482.4%
 
CM19371.9%
 
OG10331.0%
 
PO5920.6%
 
DM5490.5%
 
CH1460.1%
 
WC1350.1%
 
OT950.1%
 
MP790.1%
 
SI550.1%
 
FR1< 0.1%
 
2022-02-02T15:49:02.067988image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-02T15:49:02.179859image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.60442584
Min length1

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M4481027.4%
 
C4184525.6%
 
?4025624.7%
 
P82115.0%
 
H64203.9%
 
S50623.1%
 
B46552.9%
 
D40812.5%
 
U24481.5%
 
N24481.5%
 
O17201.1%
 
G10330.6%
 
W1350.1%
 
T950.1%
 
I55< 0.1%
 
F1< 0.1%
 
R1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter12302075.3%
 
Other Punctuation4025624.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?40256100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M4481036.4%
 
C4184534.0%
 
P82116.7%
 
H64205.2%
 
S50624.1%
 
B46553.8%
 
D40813.3%
 
U24482.0%
 
N24482.0%
 
O17201.4%
 
G10330.8%
 
W1350.1%
 
T950.1%
 
I55< 0.1%
 
F1< 0.1%
 
R1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12302075.3%
 
Common4025624.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
?40256100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M4481036.4%
 
C4184534.0%
 
P82116.7%
 
H64205.2%
 
S50624.1%
 
B46553.8%
 
D40813.3%
 
U24482.0%
 
N24482.0%
 
O17201.4%
 
G10330.8%
 
W1350.1%
 
T950.1%
 
I55< 0.1%
 
F1< 0.1%
 
R1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII163276100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M4481027.4%
 
C4184525.6%
 
?4025624.7%
 
P82115.0%
 
H64203.9%
 
S50623.1%
 
B46552.9%
 
D40812.5%
 
U24481.5%
 
N24481.5%
 
O17201.1%
 
G10330.6%
 
W1350.1%
 
T950.1%
 
I55< 0.1%
 
F1< 0.1%
 
R1< 0.1%
 

medical_specialty
Categorical

HIGH CARDINALITY

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
49949 
InternalMedicine
14635 
Emergency/Trauma
7565 
Family/GeneralPractice
7440 
Cardiology
5352 
Other values (68)
16825 
ValueCountFrequency (%) 
?4994949.1%
 
InternalMedicine1463514.4%
 
Emergency/Trauma75657.4%
 
Family/GeneralPractice74407.3%
 
Cardiology53525.3%
 
Surgery-General30993.0%
 
Nephrology16131.6%
 
Orthopedics14001.4%
 
Orthopedics-Reconstructive12331.2%
 
Radiologist11401.1%
 
Pulmonology8710.9%
 
Psychiatry8540.8%
 
Urology6850.7%
 
ObstetricsandGynecology6710.7%
 
Surgery-Cardiovascular/Thoracic6520.6%
 
Gastroenterology5640.6%
 
Surgery-Vascular5330.5%
 
Surgery-Neuro4680.5%
 
PhysicalMedicineandRehabilitation3910.4%
 
Oncology3480.3%
 
Pediatrics2540.2%
 
Hematology/Oncology2070.2%
 
Neurology2030.2%
 
Pediatrics-Endocrinology1590.2%
 
Otolaryngology1250.1%
 
Other values (48)13551.3%
 
2022-02-02T15:49:02.312690image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2022-02-02T15:49:02.453538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length8
Mean length8.612670243
Min length1

Overview of Unicode Properties

Unique unicode characters44
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e10515112.0%
 
r768998.8%
 
a711498.1%
 
n687987.8%
 
i633087.2%
 
c500075.7%
 
?499495.7%
 
l488715.6%
 
y349374.0%
 
t341493.9%
 
o340533.9%
 
d270353.1%
 
g255962.9%
 
m238462.7%
 
u168561.9%
 
/158711.8%
 
M150551.7%
 
I146831.7%
 
G118821.4%
 
s106381.2%
 
P104481.2%
 
T83321.0%
 
E78610.9%
 
F74510.9%
 
h69650.8%
 
Other values (19)366874.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter70584680.5%
 
Uppercase Letter9814811.2%
 
Other Punctuation658567.5%
 
Dash Punctuation66270.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1505515.3%
 
I1468315.0%
 
G1188212.1%
 
P1044810.6%
 
T83328.5%
 
E78618.0%
 
F74517.6%
 
C63076.4%
 
S51565.3%
 
O41464.2%
 
R28472.9%
 
N23072.4%
 
U6850.7%
 
V5330.5%
 
H3510.4%
 
A550.1%
 
D49< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e10515114.9%
 
r7689910.9%
 
a7114910.1%
 
n687989.7%
 
i633089.0%
 
c500077.1%
 
l488716.9%
 
y349374.9%
 
t341494.8%
 
o340534.8%
 
d270353.8%
 
g255963.6%
 
m238463.4%
 
u168562.4%
 
s106381.5%
 
h69651.0%
 
p44160.6%
 
v19960.3%
 
b11140.2%
 
f49< 0.1%
 
x11< 0.1%
 
w1< 0.1%
 
k1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6627100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
?4994975.8%
 
/1587124.1%
 
&360.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin80399491.7%
 
Common724838.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e10515113.1%
 
r768999.6%
 
a711498.8%
 
n687988.6%
 
i633087.9%
 
c500076.2%
 
l488716.1%
 
y349374.3%
 
t341494.2%
 
o340534.2%
 
d270353.4%
 
g255963.2%
 
m238463.0%
 
u168562.1%
 
M150551.9%
 
I146831.8%
 
G118821.5%
 
s106381.3%
 
P104481.3%
 
T83321.0%
 
E78611.0%
 
F74510.9%
 
h69650.9%
 
C63070.8%
 
S51560.6%
 
Other values (15)185612.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
?4994968.9%
 
/1587121.9%
 
-66279.1%
 
&36< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII876477100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e10515112.0%
 
r768998.8%
 
a711498.1%
 
n687987.8%
 
i633087.2%
 
c500075.7%
 
?499495.7%
 
l488715.6%
 
y349374.0%
 
t341493.9%
 
o340533.9%
 
d270353.1%
 
g255962.9%
 
m238462.7%
 
u168561.9%
 
/158711.8%
 
M150551.7%
 
I146831.7%
 
G118821.4%
 
s106381.2%
 
P104481.2%
 
T83321.0%
 
E78610.9%
 
F74510.9%
 
h69650.8%
 
Other values (19)366874.2%
 

num_lab_procedures
Real number (ℝ≥0)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.09564098
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:02.833100image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.67436225
Coefficient of variation (CV)0.4565278947
Kurtosis-0.2450735189
Mean43.09564098
Median Absolute Deviation (MAD)13
Skewness-0.2365439206
Sum4385671
Variance387.0805299
MonotocityNot monotonic
2022-02-02T15:49:02.982907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132083.2%
 
4328042.8%
 
4424962.5%
 
4523762.3%
 
3822132.2%
 
4022012.2%
 
4621892.2%
 
4121172.1%
 
4221132.1%
 
4721062.1%
 
3921012.1%
 
3720792.0%
 
4920662.0%
 
4820582.0%
 
3619621.9%
 
5119251.9%
 
5019241.9%
 
3519071.9%
 
5418881.9%
 
5618391.8%
 
5218381.8%
 
5518361.8%
 
5318021.8%
 
5717471.7%
 
5817081.7%
 
Other values (93)4926348.4%
 
ValueCountFrequency (%) 
132083.2%
 
211011.1%
 
36680.7%
 
43780.4%
 
52860.3%
 
62820.3%
 
73230.3%
 
83660.4%
 
99330.9%
 
108380.8%
 
ValueCountFrequency (%) 
1321< 0.1%
 
1291< 0.1%
 
1261< 0.1%
 
1211< 0.1%
 
1201< 0.1%
 
1181< 0.1%
 
1142< 0.1%
 
1133< 0.1%
 
1113< 0.1%
 
1094< 0.1%
 

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339730362
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Memory size795.2 KiB
2022-02-02T15:49:03.105764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705806979
Coefficient of variation (CV)1.273246489
Kurtosis0.8571103021
Mean1.339730362
Median Absolute Deviation (MAD)1
Skewness1.316414763
Sum136339
Variance2.90977745
MonotocityNot monotonic
2022-02-02T15:49:03.189665image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
04665245.8%
 
12074220.4%
 
21271712.5%
 
394439.3%
 
649544.9%
 
441804.1%
 
530783.0%
 
ValueCountFrequency (%) 
04665245.8%
 
12074220.4%
 
21271712.5%
 
394439.3%
 
441804.1%
 
530783.0%
 
649544.9%
 
ValueCountFrequency (%) 
649544.9%
 
530783.0%
 
441804.1%
 
394439.3%
 
21271712.5%
 
12074220.4%
 
04665245.8%
 

num_medications
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.02184423
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:03.313522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.127566209
Coefficient of variation (CV)0.5072803163
Kurtosis3.468154915
Mean16.02184423
Median Absolute Deviation (MAD)5
Skewness1.326672134
Sum1630479
Variance66.05733248
MonotocityNot monotonic
2022-02-02T15:49:03.456366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1360866.0%
 
1260045.9%
 
1157955.7%
 
1557925.7%
 
1457075.6%
 
1654305.3%
 
1053465.3%
 
1749194.8%
 
949134.8%
 
1845234.4%
 
843534.3%
 
1940784.0%
 
2036913.6%
 
734843.4%
 
2132303.2%
 
2228682.8%
 
626992.7%
 
2324262.4%
 
2421092.1%
 
520172.0%
 
2518881.9%
 
2616081.6%
 
2714321.4%
 
414171.4%
 
2812331.2%
 
Other values (50)87188.6%
 
ValueCountFrequency (%) 
12620.3%
 
24700.5%
 
39000.9%
 
414171.4%
 
520172.0%
 
626992.7%
 
734843.4%
 
843534.3%
 
949134.8%
 
1053465.3%
 
ValueCountFrequency (%) 
811< 0.1%
 
791< 0.1%
 
752< 0.1%
 
741< 0.1%
 
723< 0.1%
 
702< 0.1%
 
695< 0.1%
 
687< 0.1%
 
677< 0.1%
 
665< 0.1%
 

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3693571527
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Memory size795.2 KiB
2022-02-02T15:49:03.595192image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.267265097
Coefficient of variation (CV)3.431001911
Kurtosis147.9077363
Mean0.3693571527
Median Absolute Deviation (MAD)0
Skewness8.832958927
Sum37588
Variance1.605960825
MonotocityNot monotonic
2022-02-02T15:49:03.710070image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
08502783.6%
 
185478.4%
 
235943.5%
 
320422.0%
 
410991.1%
 
55330.5%
 
63030.3%
 
71550.2%
 
8980.1%
 
9830.1%
 
10570.1%
 
1142< 0.1%
 
1331< 0.1%
 
1230< 0.1%
 
1428< 0.1%
 
1520< 0.1%
 
1615< 0.1%
 
178< 0.1%
 
217< 0.1%
 
207< 0.1%
 
185< 0.1%
 
225< 0.1%
 
193< 0.1%
 
273< 0.1%
 
243< 0.1%
 
Other values (14)21< 0.1%
 
ValueCountFrequency (%) 
08502783.6%
 
185478.4%
 
235943.5%
 
320422.0%
 
410991.1%
 
55330.5%
 
63030.3%
 
71550.2%
 
8980.1%
 
9830.1%
 
ValueCountFrequency (%) 
421< 0.1%
 
401< 0.1%
 
391< 0.1%
 
381< 0.1%
 
371< 0.1%
 
362< 0.1%
 
352< 0.1%
 
341< 0.1%
 
332< 0.1%
 
292< 0.1%
 

number_emergency
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1978362125
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Memory size795.2 KiB
2022-02-02T15:49:03.827937image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9304722684
Coefficient of variation (CV)4.703245461
Kurtosis1191.686726
Mean0.1978362125
Median Absolute Deviation (MAD)0
Skewness22.85558215
Sum20133
Variance0.8657786423
MonotocityNot monotonic
2022-02-02T15:49:03.939801image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
09038388.8%
 
176777.5%
 
220422.0%
 
37250.7%
 
43740.4%
 
51920.2%
 
6940.1%
 
7730.1%
 
850< 0.1%
 
1034< 0.1%
 
933< 0.1%
 
1123< 0.1%
 
1312< 0.1%
 
1210< 0.1%
 
226< 0.1%
 
165< 0.1%
 
185< 0.1%
 
194< 0.1%
 
204< 0.1%
 
153< 0.1%
 
143< 0.1%
 
252< 0.1%
 
212< 0.1%
 
281< 0.1%
 
421< 0.1%
 
Other values (8)8< 0.1%
 
ValueCountFrequency (%) 
09038388.8%
 
176777.5%
 
220422.0%
 
37250.7%
 
43740.4%
 
51920.2%
 
6940.1%
 
7730.1%
 
850< 0.1%
 
933< 0.1%
 
ValueCountFrequency (%) 
761< 0.1%
 
641< 0.1%
 
631< 0.1%
 
541< 0.1%
 
461< 0.1%
 
421< 0.1%
 
371< 0.1%
 
291< 0.1%
 
281< 0.1%
 
252< 0.1%
 

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6355659061
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Memory size795.2 KiB
2022-02-02T15:49:04.056665image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.26286329
Coefficient of variation (CV)1.986990299
Kurtosis20.71939695
Mean0.6355659061
Median Absolute Deviation (MAD)0
Skewness3.614138992
Sum64679
Variance1.594823689
MonotocityNot monotonic
2022-02-02T15:49:04.155537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
06763066.5%
 
11952119.2%
 
275667.4%
 
334113.4%
 
416221.6%
 
58120.8%
 
64800.5%
 
72680.3%
 
81510.1%
 
91110.1%
 
10610.1%
 
1149< 0.1%
 
1234< 0.1%
 
1320< 0.1%
 
1410< 0.1%
 
159< 0.1%
 
166< 0.1%
 
192< 0.1%
 
171< 0.1%
 
211< 0.1%
 
181< 0.1%
 
ValueCountFrequency (%) 
06763066.5%
 
11952119.2%
 
275667.4%
 
334113.4%
 
416221.6%
 
58120.8%
 
64800.5%
 
72680.3%
 
81510.1%
 
91110.1%
 
ValueCountFrequency (%) 
211< 0.1%
 
192< 0.1%
 
181< 0.1%
 
171< 0.1%
 
166< 0.1%
 
159< 0.1%
 
1410< 0.1%
 
1320< 0.1%
 
1234< 0.1%
 
1149< 0.1%
 

diag_1
Categorical

HIGH CARDINALITY

Distinct717
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
428
 
6862
414
 
6581
786
 
4016
410
 
3614
486
 
3508
Other values (712)
77185 
ValueCountFrequency (%) 
42868626.7%
 
41465816.5%
 
78640163.9%
 
41036143.6%
 
48635083.4%
 
42727662.7%
 
49122752.2%
 
71521512.1%
 
68220422.0%
 
43420282.0%
 
78020192.0%
 
99619671.9%
 
27618891.9%
 
3816881.7%
 
250.816801.7%
 
59915951.6%
 
58415201.5%
 
V5712071.2%
 
250.611831.2%
 
51811151.1%
 
82010821.1%
 
57710571.0%
 
49310561.0%
 
43510161.0%
 
5629891.0%
 
Other values (692)4486044.1%
 
2022-02-02T15:49:04.297371image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique82 ?
Unique (%)0.1%
2022-02-02T15:49:04.425238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.175215691
Min length1

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
45545717.2%
 
23987612.3%
 
83794911.7%
 
53713111.5%
 
7286688.9%
 
1281068.7%
 
0249607.7%
 
6231987.2%
 
9199786.2%
 
3176185.5%
 
.85222.6%
 
V16440.5%
 
?21< 0.1%
 
E1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number31294196.8%
 
Other Punctuation85432.6%
 
Uppercase Letter16450.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
45545717.7%
 
23987612.7%
 
83794912.1%
 
53713111.9%
 
7286689.2%
 
1281069.0%
 
0249608.0%
 
6231987.4%
 
9199786.4%
 
3176185.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.852299.8%
 
?210.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V164499.9%
 
E10.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common32148499.5%
 
Latin16450.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
45545717.3%
 
23987612.4%
 
83794911.8%
 
53713111.5%
 
7286688.9%
 
1281068.7%
 
0249607.8%
 
6231987.2%
 
9199786.2%
 
3176185.5%
 
.85222.7%
 
?21< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V164499.9%
 
E10.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323129100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
45545717.2%
 
23987612.3%
 
83794911.7%
 
53713111.5%
 
7286688.9%
 
1281068.7%
 
0249607.7%
 
6231987.2%
 
9199786.2%
 
3176185.5%
 
.85222.6%
 
V16440.5%
 
?21< 0.1%
 
E1< 0.1%
 

diag_2
Categorical

HIGH CARDINALITY

Distinct749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
276
 
6752
428
 
6662
250
 
6071
427
 
5036
401
 
3736
Other values (744)
73509 
ValueCountFrequency (%) 
27667526.6%
 
42866626.5%
 
25060716.0%
 
42750364.9%
 
40137363.7%
 
49633053.2%
 
59932883.2%
 
40328232.8%
 
41426502.6%
 
41125662.5%
 
250.0220742.0%
 
70719992.0%
 
58518711.8%
 
58416491.6%
 
49115451.5%
 
250.0115231.5%
 
28515201.5%
 
78014911.5%
 
42514341.4%
 
68214331.4%
 
48613791.4%
 
51813551.3%
 
42410711.1%
 
41310421.0%
 
250.68950.9%
 
Other values (724)3659636.0%
 
2022-02-02T15:49:04.561064image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique124 ?
Unique (%)0.1%
2022-02-02T15:49:04.686929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.166194996
Min length1

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
45115515.9%
 
24976515.4%
 
53817611.8%
 
03404610.6%
 
8287118.9%
 
7286548.9%
 
1261588.1%
 
9218426.8%
 
6199906.2%
 
3140974.4%
 
.67232.1%
 
V18050.6%
 
E7310.2%
 
?3580.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number31259497.0%
 
Other Punctuation70812.2%
 
Uppercase Letter25360.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.672394.9%
 
?3585.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
45115516.4%
 
24976515.9%
 
53817612.2%
 
03404610.9%
 
8287119.2%
 
7286549.2%
 
1261588.4%
 
9218427.0%
 
6199906.4%
 
3140974.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V180571.2%
 
E73128.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common31967599.2%
 
Latin25360.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
45115516.0%
 
24976515.6%
 
53817611.9%
 
03404610.7%
 
8287119.0%
 
7286549.0%
 
1261588.2%
 
9218426.8%
 
6199906.3%
 
3140974.4%
 
.67232.1%
 
?3580.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V180571.2%
 
E73128.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII322211100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
45115515.9%
 
24976515.4%
 
53817611.8%
 
03404610.6%
 
8287118.9%
 
7286548.9%
 
1261588.1%
 
9218426.8%
 
6199906.2%
 
3140974.4%
 
.67232.1%
 
V18050.6%
 
E7310.2%
 
?3580.1%
 

diag_3
Categorical

HIGH CARDINALITY

Distinct790
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
250
11555 
401
8289 
276
 
5175
428
 
4577
427
 
3955
Other values (785)
68215 
ValueCountFrequency (%) 
2501155511.4%
 
40182898.1%
 
27651755.1%
 
42845774.5%
 
42739553.9%
 
41436643.6%
 
49626052.6%
 
40323572.3%
 
58519922.0%
 
27219691.9%
 
59919411.9%
 
?14231.4%
 
V4513891.4%
 
250.0213691.3%
 
70713601.3%
 
78013341.3%
 
28512001.2%
 
42511361.1%
 
250.610801.1%
 
42410631.0%
 
5849630.9%
 
3059240.9%
 
250.019150.9%
 
6828870.9%
 
5188540.8%
 
Other values (765)3779037.1%
 
2022-02-02T15:49:04.821771image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique122 ?
Unique (%)0.1%
2022-02-02T15:49:04.947624image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.111658118
Min length1

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
25124416.2%
 
44925215.6%
 
54126013.0%
 
03971112.5%
 
7265048.4%
 
1246847.8%
 
8238257.5%
 
9173235.5%
 
6164415.2%
 
3143334.5%
 
.56031.8%
 
V38141.2%
 
?14230.4%
 
E12440.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number30457796.2%
 
Other Punctuation70262.2%
 
Uppercase Letter50581.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.560379.7%
 
?142320.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
25124416.8%
 
44925216.2%
 
54126013.5%
 
03971113.0%
 
7265048.7%
 
1246848.1%
 
8238257.8%
 
9173235.7%
 
6164415.4%
 
3143334.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V381475.4%
 
E124424.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common31160398.4%
 
Latin50581.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
25124416.4%
 
44925215.8%
 
54126013.2%
 
03971112.7%
 
7265048.5%
 
1246847.9%
 
8238257.6%
 
9173235.6%
 
6164415.3%
 
3143334.6%
 
.56031.8%
 
?14230.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V381475.4%
 
E124424.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII316661100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
25124416.2%
 
44925215.6%
 
54126013.0%
 
03971112.5%
 
7265048.4%
 
1246847.8%
 
8238257.5%
 
9173235.5%
 
6164415.2%
 
3143334.5%
 
.56031.8%
 
V38141.2%
 
?14230.4%
 
E12440.4%
 

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.422606765
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size795.2 KiB
2022-02-02T15:49:05.040515image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.933600145
Coefficient of variation (CV)0.2605014931
Kurtosis-0.07905602427
Mean7.422606765
Median Absolute Deviation (MAD)1
Skewness-0.8767462388
Sum755369
Variance3.738809521
MonotocityNot monotonic
2022-02-02T15:49:05.132396image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
94947448.6%
 
51139311.2%
 
81061610.4%
 
71039310.2%
 
61016110.0%
 
455375.4%
 
328352.8%
 
210231.0%
 
12190.2%
 
1645< 0.1%
 
1017< 0.1%
 
1316< 0.1%
 
1111< 0.1%
 
1510< 0.1%
 
129< 0.1%
 
147< 0.1%
 
ValueCountFrequency (%) 
12190.2%
 
210231.0%
 
328352.8%
 
455375.4%
 
51139311.2%
 
61016110.0%
 
71039310.2%
 
81061610.4%
 
94947448.6%
 
1017< 0.1%
 
ValueCountFrequency (%) 
1645< 0.1%
 
1510< 0.1%
 
147< 0.1%
 
1316< 0.1%
 
129< 0.1%
 
1111< 0.1%
 
1017< 0.1%
 
94947448.6%
 
81061610.4%
 
71039310.2%
 

max_glu_serum
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
None
96420 
Norm
 
2597
>200
 
1485
>300
 
1264
ValueCountFrequency (%) 
None9642094.7%
 
Norm25972.6%
 
>20014851.5%
 
>30012641.2%
 
2022-02-02T15:49:05.243278image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:05.315195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:05.392105image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N9901724.3%
 
o9901724.3%
 
n9642023.7%
 
e9642023.7%
 
054981.4%
 
>27490.7%
 
r25970.6%
 
m25970.6%
 
214850.4%
 
312640.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter29705173.0%
 
Uppercase Letter9901724.3%
 
Decimal Number82472.0%
 
Math Symbol27490.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N99017100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9901733.3%
 
n9642032.5%
 
e9642032.5%
 
r25970.9%
 
m25970.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>2749100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0549866.7%
 
2148518.0%
 
3126415.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin39606897.3%
 
Common109962.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N9901725.0%
 
o9901725.0%
 
n9642024.3%
 
e9642024.3%
 
r25970.7%
 
m25970.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
0549850.0%
 
>274925.0%
 
2148513.5%
 
3126411.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII407064100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N9901724.3%
 
o9901724.3%
 
n9642023.7%
 
e9642023.7%
 
054981.4%
 
>27490.7%
 
r25970.6%
 
m25970.6%
 
214850.4%
 
312640.3%
 

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
None
84748 
>8
 
8216
Norm
 
4990
>7
 
3812
ValueCountFrequency (%) 
None8474883.3%
 
>882168.1%
 
Norm49904.9%
 
>738123.7%
 
2022-02-02T15:49:05.496986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:05.576876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:05.659792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.763614567
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N8973823.4%
 
o8973823.4%
 
n8474822.1%
 
e8474822.1%
 
>120283.1%
 
882162.1%
 
r49901.3%
 
m49901.3%
 
738121.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter26921470.3%
 
Uppercase Letter8973823.4%
 
Math Symbol120283.1%
 
Decimal Number120283.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N89738100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o8973833.3%
 
n8474831.5%
 
e8474831.5%
 
r49901.9%
 
m49901.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>12028100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
8821668.3%
 
7381231.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35895293.7%
 
Common240566.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N8973825.0%
 
o8973825.0%
 
n8474823.6%
 
e8474823.6%
 
r49901.4%
 
m49901.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
>1202850.0%
 
8821634.2%
 
7381215.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII383008100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N8973823.4%
 
o8973823.4%
 
n8474822.1%
 
e8474822.1%
 
>120283.1%
 
882162.1%
 
r49901.3%
 
m49901.3%
 
738121.0%
 

metformin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
81778 
Steady
18346 
Up
 
1067
Down
 
575
ValueCountFrequency (%) 
No8177880.4%
 
Steady1834618.0%
 
Up10671.0%
 
Down5750.6%
 
2022-02-02T15:49:05.774645image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:05.853565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:05.943448image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.732405715
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o8235329.6%
 
N8177829.4%
 
S183466.6%
 
t183466.6%
 
e183466.6%
 
a183466.6%
 
d183466.6%
 
y183466.6%
 
U10670.4%
 
p10670.4%
 
D5750.2%
 
w5750.2%
 
n5750.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter17630063.4%
 
Uppercase Letter10176636.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N8177880.4%
 
S1834618.0%
 
U10671.0%
 
D5750.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o8235346.7%
 
t1834610.4%
 
e1834610.4%
 
a1834610.4%
 
d1834610.4%
 
y1834610.4%
 
p10670.6%
 
w5750.3%
 
n5750.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin278066100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o8235329.6%
 
N8177829.4%
 
S183466.6%
 
t183466.6%
 
e183466.6%
 
a183466.6%
 
d183466.6%
 
y183466.6%
 
U10670.4%
 
p10670.4%
 
D5750.2%
 
w5750.2%
 
n5750.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII278066100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o8235329.6%
 
N8177829.4%
 
S183466.6%
 
t183466.6%
 
e183466.6%
 
a183466.6%
 
d183466.6%
 
y183466.6%
 
U10670.4%
 
p10670.4%
 
D5750.2%
 
w5750.2%
 
n5750.2%
 

repaglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
100227 
Steady
 
1384
Up
 
110
Down
 
45
ValueCountFrequency (%) 
No10022798.5%
 
Steady13841.4%
 
Up1100.1%
 
Down45< 0.1%
 
2022-02-02T15:49:06.058326image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:06.130246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:06.215131image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.05528369
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10027247.9%
 
N10022747.9%
 
S13840.7%
 
t13840.7%
 
e13840.7%
 
a13840.7%
 
d13840.7%
 
y13840.7%
 
U1100.1%
 
p1100.1%
 
D45< 0.1%
 
w45< 0.1%
 
n45< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10739251.3%
 
Uppercase Letter10176648.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10022798.5%
 
S13841.4%
 
U1100.1%
 
D45< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10027293.4%
 
t13841.3%
 
e13841.3%
 
a13841.3%
 
d13841.3%
 
y13841.3%
 
p1100.1%
 
w45< 0.1%
 
n45< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin209158100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10027247.9%
 
N10022747.9%
 
S13840.7%
 
t13840.7%
 
e13840.7%
 
a13840.7%
 
d13840.7%
 
y13840.7%
 
U1100.1%
 
p1100.1%
 
D45< 0.1%
 
w45< 0.1%
 
n45< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII209158100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10027247.9%
 
N10022747.9%
 
S13840.7%
 
t13840.7%
 
e13840.7%
 
a13840.7%
 
d13840.7%
 
y13840.7%
 
U1100.1%
 
p1100.1%
 
D45< 0.1%
 
w45< 0.1%
 
n45< 0.1%
 

nateglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101063 
Steady
 
668
Up
 
24
Down
 
11
ValueCountFrequency (%) 
No10106399.3%
 
Steady6680.7%
 
Up24< 0.1%
 
Down11< 0.1%
 
2022-02-02T15:49:06.325002image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:06.395931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:06.481819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.026472496
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10107449.0%
 
N10106349.0%
 
S6680.3%
 
t6680.3%
 
e6680.3%
 
a6680.3%
 
d6680.3%
 
y6680.3%
 
U24< 0.1%
 
p24< 0.1%
 
D11< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10446050.7%
 
Uppercase Letter10176649.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10106399.3%
 
S6680.7%
 
U24< 0.1%
 
D11< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10107496.8%
 
t6680.6%
 
e6680.6%
 
a6680.6%
 
d6680.6%
 
y6680.6%
 
p24< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin206226100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10107449.0%
 
N10106349.0%
 
S6680.3%
 
t6680.3%
 
e6680.3%
 
a6680.3%
 
d6680.3%
 
y6680.3%
 
U24< 0.1%
 
p24< 0.1%
 
D11< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII206226100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10107449.0%
 
N10106349.0%
 
S6680.3%
 
t6680.3%
 
e6680.3%
 
a6680.3%
 
d6680.3%
 
y6680.3%
 
U24< 0.1%
 
p24< 0.1%
 
D11< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

chlorpropamide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101680 
Steady
 
79
Up
 
6
Down
 
1
ValueCountFrequency (%) 
No10168099.9%
 
Steady790.1%
 
Up6< 0.1%
 
Down1< 0.1%
 
2022-02-02T15:49:06.591703image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-02T15:49:06.662620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:06.746510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.003124816
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10168149.9%
 
N10168049.9%
 
S79< 0.1%
 
t79< 0.1%
 
e79< 0.1%
 
a79< 0.1%
 
d79< 0.1%
 
y79< 0.1%
 
U6< 0.1%
 
p6< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10208450.1%
 
Uppercase Letter10176649.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10168099.9%
 
S790.1%
 
U6< 0.1%
 
D1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10168199.6%
 
t790.1%
 
e790.1%
 
a790.1%
 
d790.1%
 
y790.1%
 
p6< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203850100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10168149.9%
 
N10168049.9%
 
S79< 0.1%
 
t79< 0.1%
 
e79< 0.1%
 
a79< 0.1%
 
d79< 0.1%
 
y79< 0.1%
 
U6< 0.1%
 
p6< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203850100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10168149.9%
 
N10168049.9%
 
S79< 0.1%
 
t79< 0.1%
 
e79< 0.1%
 
a79< 0.1%
 
d79< 0.1%
 
y79< 0.1%
 
U6< 0.1%
 
p6< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

glimepiride
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
96575 
Steady
 
4670
Up
 
327
Down
 
194
ValueCountFrequency (%) 
No9657594.9%
 
Steady46704.6%
 
Up3270.3%
 
Down1940.2%
 
2022-02-02T15:49:06.857393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:06.927311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:07.013211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.187371028
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9676943.5%
 
N9657543.4%
 
S46702.1%
 
t46702.1%
 
e46702.1%
 
a46702.1%
 
d46702.1%
 
y46702.1%
 
U3270.1%
 
p3270.1%
 
D1940.1%
 
w1940.1%
 
n1940.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12083454.3%
 
Uppercase Letter10176645.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9657594.9%
 
S46704.6%
 
U3270.3%
 
D1940.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9676980.1%
 
t46703.9%
 
e46703.9%
 
a46703.9%
 
d46703.9%
 
y46703.9%
 
p3270.3%
 
w1940.2%
 
n1940.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin222600100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9676943.5%
 
N9657543.4%
 
S46702.1%
 
t46702.1%
 
e46702.1%
 
a46702.1%
 
d46702.1%
 
y46702.1%
 
U3270.1%
 
p3270.1%
 
D1940.1%
 
w1940.1%
 
n1940.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII222600100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9676943.5%
 
N9657543.4%
 
S46702.1%
 
t46702.1%
 
e46702.1%
 
a46702.1%
 
d46702.1%
 
y46702.1%
 
U3270.1%
 
p3270.1%
 
D1940.1%
 
w1940.1%
 
n1940.1%
 

acetohexamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1
ValueCountFrequency (%) 
No101765> 99.9%
 
Steady1< 0.1%
 
2022-02-02T15:49:07.120073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-02T15:49:07.184997image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:07.256926image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177050.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101765> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101765> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203536100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203536100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

glipizide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
89080 
Steady
11356 
Up
 
770
Down
 
560
ValueCountFrequency (%) 
No8908087.5%
 
Steady1135611.2%
 
Up7700.8%
 
Down5600.6%
 
2022-02-02T15:49:07.364787image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:07.431709image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:07.517609image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.45736297
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o8964035.8%
 
N8908035.6%
 
S113564.5%
 
t113564.5%
 
e113564.5%
 
a113564.5%
 
d113564.5%
 
y113564.5%
 
U7700.3%
 
p7700.3%
 
D5600.2%
 
w5600.2%
 
n5600.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14831059.3%
 
Uppercase Letter10176640.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N8908087.5%
 
S1135611.2%
 
U7700.8%
 
D5600.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o8964060.4%
 
t113567.7%
 
e113567.7%
 
a113567.7%
 
d113567.7%
 
y113567.7%
 
p7700.5%
 
w5600.4%
 
n5600.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin250076100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o8964035.8%
 
N8908035.6%
 
S113564.5%
 
t113564.5%
 
e113564.5%
 
a113564.5%
 
d113564.5%
 
y113564.5%
 
U7700.3%
 
p7700.3%
 
D5600.2%
 
w5600.2%
 
n5600.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII250076100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o8964035.8%
 
N8908035.6%
 
S113564.5%
 
t113564.5%
 
e113564.5%
 
a113564.5%
 
d113564.5%
 
y113564.5%
 
U7700.3%
 
p7700.3%
 
D5600.2%
 
w5600.2%
 
n5600.2%
 

glyburide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
91116 
Steady
9274 
Up
 
812
Down
 
564
ValueCountFrequency (%) 
No9111689.5%
 
Steady92749.1%
 
Up8120.8%
 
Down5640.6%
 
2022-02-02T15:49:07.623497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:07.690419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:07.775320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.375606784
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9168037.9%
 
N9111637.7%
 
S92743.8%
 
t92743.8%
 
e92743.8%
 
a92743.8%
 
d92743.8%
 
y92743.8%
 
U8120.3%
 
p8120.3%
 
D5640.2%
 
w5640.2%
 
n5640.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter13999057.9%
 
Uppercase Letter10176642.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9111689.5%
 
S92749.1%
 
U8120.8%
 
D5640.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9168065.5%
 
t92746.6%
 
e92746.6%
 
a92746.6%
 
d92746.6%
 
y92746.6%
 
p8120.6%
 
w5640.4%
 
n5640.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin241756100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9168037.9%
 
N9111637.7%
 
S92743.8%
 
t92743.8%
 
e92743.8%
 
a92743.8%
 
d92743.8%
 
y92743.8%
 
U8120.3%
 
p8120.3%
 
D5640.2%
 
w5640.2%
 
n5640.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII241756100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9168037.9%
 
N9111637.7%
 
S92743.8%
 
t92743.8%
 
e92743.8%
 
a92743.8%
 
d92743.8%
 
y92743.8%
 
U8120.3%
 
p8120.3%
 
D5640.2%
 
w5640.2%
 
n5640.2%
 

tolbutamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101743 
Steady
 
23
ValueCountFrequency (%) 
No101743> 99.9%
 
Steady23< 0.1%
 
2022-02-02T15:49:07.881184image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:07.947107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:08.018024image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000904035
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10174350.0%
 
o10174350.0%
 
S23< 0.1%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10185850.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101743> 99.9%
 
S23< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10174399.9%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203624100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10174350.0%
 
o10174350.0%
 
S23< 0.1%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203624100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10174350.0%
 
o10174350.0%
 
S23< 0.1%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

pioglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
94438 
Steady
 
6976
Up
 
234
Down
 
118
ValueCountFrequency (%) 
No9443892.8%
 
Steady69766.9%
 
Up2340.2%
 
Down1180.1%
 
2022-02-02T15:49:08.126909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:08.194817image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:08.278719image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.276516715
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9455640.8%
 
N9443840.8%
 
S69763.0%
 
t69763.0%
 
e69763.0%
 
a69763.0%
 
d69763.0%
 
y69763.0%
 
U2340.1%
 
p2340.1%
 
D1180.1%
 
w1180.1%
 
n1180.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12990656.1%
 
Uppercase Letter10176643.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9443892.8%
 
S69766.9%
 
U2340.2%
 
D1180.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9455672.8%
 
t69765.4%
 
e69765.4%
 
a69765.4%
 
d69765.4%
 
y69765.4%
 
p2340.2%
 
w1180.1%
 
n1180.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin231672100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9455640.8%
 
N9443840.8%
 
S69763.0%
 
t69763.0%
 
e69763.0%
 
a69763.0%
 
d69763.0%
 
y69763.0%
 
U2340.1%
 
p2340.1%
 
D1180.1%
 
w1180.1%
 
n1180.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII231672100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9455640.8%
 
N9443840.8%
 
S69763.0%
 
t69763.0%
 
e69763.0%
 
a69763.0%
 
d69763.0%
 
y69763.0%
 
U2340.1%
 
p2340.1%
 
D1180.1%
 
w1180.1%
 
n1180.1%
 

rosiglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
95401 
Steady
 
6100
Up
 
178
Down
 
87
ValueCountFrequency (%) 
No9540193.7%
 
Steady61006.0%
 
Up1780.2%
 
Down870.1%
 
2022-02-02T15:49:08.386593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:08.457510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:08.543410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.241475542
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9548841.9%
 
N9540141.8%
 
S61002.7%
 
t61002.7%
 
e61002.7%
 
a61002.7%
 
d61002.7%
 
y61002.7%
 
U1780.1%
 
p1780.1%
 
D87< 0.1%
 
w87< 0.1%
 
n87< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12634055.4%
 
Uppercase Letter10176644.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9540193.7%
 
S61006.0%
 
U1780.2%
 
D870.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9548875.6%
 
t61004.8%
 
e61004.8%
 
a61004.8%
 
d61004.8%
 
y61004.8%
 
p1780.1%
 
w870.1%
 
n870.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin228106100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9548841.9%
 
N9540141.8%
 
S61002.7%
 
t61002.7%
 
e61002.7%
 
a61002.7%
 
d61002.7%
 
y61002.7%
 
U1780.1%
 
p1780.1%
 
D87< 0.1%
 
w87< 0.1%
 
n87< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII228106100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9548841.9%
 
N9540141.8%
 
S61002.7%
 
t61002.7%
 
e61002.7%
 
a61002.7%
 
d61002.7%
 
y61002.7%
 
U1780.1%
 
p1780.1%
 
D87< 0.1%
 
w87< 0.1%
 
n87< 0.1%
 

acarbose
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101458 
Steady
 
295
Up
 
10
Down
 
3
ValueCountFrequency (%) 
No10145899.7%
 
Steady2950.3%
 
Up10< 0.1%
 
Down3< 0.1%
 
2022-02-02T15:49:08.653281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:08.724211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:08.809100image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.011654187
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10146149.6%
 
N10145849.6%
 
S2950.1%
 
t2950.1%
 
e2950.1%
 
a2950.1%
 
d2950.1%
 
y2950.1%
 
U10< 0.1%
 
p10< 0.1%
 
D3< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10295250.3%
 
Uppercase Letter10176649.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10145899.7%
 
S2950.3%
 
U10< 0.1%
 
D3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10146198.6%
 
t2950.3%
 
e2950.3%
 
a2950.3%
 
d2950.3%
 
y2950.3%
 
p10< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin204718100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10146149.6%
 
N10145849.6%
 
S2950.1%
 
t2950.1%
 
e2950.1%
 
a2950.1%
 
d2950.1%
 
y2950.1%
 
U10< 0.1%
 
p10< 0.1%
 
D3< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII204718100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10146149.6%
 
N10145849.6%
 
S2950.1%
 
t2950.1%
 
e2950.1%
 
a2950.1%
 
d2950.1%
 
y2950.1%
 
U10< 0.1%
 
p10< 0.1%
 
D3< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

miglitol
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101728 
Steady
 
31
Down
 
5
Up
 
2
ValueCountFrequency (%) 
No101728> 99.9%
 
Steady31< 0.1%
 
Down5< 0.1%
 
Up2< 0.1%
 
2022-02-02T15:49:08.919970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:08.990899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:09.076799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001316746
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10173350.0%
 
N10172849.9%
 
S31< 0.1%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10190050.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101728> 99.9%
 
S31< 0.1%
 
D5< 0.1%
 
U2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10173399.8%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
p2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203666100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10173350.0%
 
N10172849.9%
 
S31< 0.1%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203666100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10173350.0%
 
N10172849.9%
 
S31< 0.1%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

troglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101763 
Steady
 
3
ValueCountFrequency (%) 
No101763> 99.9%
 
Steady3< 0.1%
 
2022-02-02T15:49:09.184661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:09.250584image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:09.629154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000117918
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176350.0%
 
o10176350.0%
 
S3< 0.1%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177850.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101763> 99.9%
 
S3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101763> 99.9%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203544100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176350.0%
 
o10176350.0%
 
S3< 0.1%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203544100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176350.0%
 
o10176350.0%
 
S3< 0.1%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

tolazamide
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101727 
Steady
 
38
Up
 
1
ValueCountFrequency (%) 
No101727> 99.9%
 
Steady38< 0.1%
 
Up1< 0.1%
 
2022-02-02T15:49:09.741010image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-02T15:49:09.813925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:09.892833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001493623
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10172749.9%
 
o10172749.9%
 
S38< 0.1%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10191850.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101727> 99.9%
 
S38< 0.1%
 
U1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10172799.8%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
p1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203684100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10172749.9%
 
o10172749.9%
 
S38< 0.1%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203684100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10172749.9%
 
o10172749.9%
 
S38< 0.1%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

examide
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101766 
ValueCountFrequency (%) 
No101766100.0%
 
2022-02-02T15:49:09.997710image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:10.063647image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:10.120567image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176650.0%
 
o10176650.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10176650.0%
 
Lowercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101766100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101766100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203532100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176650.0%
 
o10176650.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203532100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176650.0%
 
o10176650.0%
 

citoglipton
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101766 
ValueCountFrequency (%) 
No101766100.0%
 
2022-02-02T15:49:10.216455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:10.282390image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:10.340323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176650.0%
 
o10176650.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10176650.0%
 
Lowercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101766100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101766100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203532100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176650.0%
 
o10176650.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203532100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176650.0%
 
o10176650.0%
 

insulin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
47383 
Steady
30849 
Down
12218 
Up
11316 
ValueCountFrequency (%) 
No4738346.6%
 
Steady3084930.3%
 
Down1221812.0%
 
Up1131611.1%
 
2022-02-02T15:49:10.447202image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:10.528091image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:10.616000image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length3.45266592
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o5960117.0%
 
N4738313.5%
 
S308498.8%
 
t308498.8%
 
e308498.8%
 
a308498.8%
 
d308498.8%
 
y308498.8%
 
D122183.5%
 
w122183.5%
 
n122183.5%
 
U113163.2%
 
p113163.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter24959871.0%
 
Uppercase Letter10176629.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N4738346.6%
 
S3084930.3%
 
D1221812.0%
 
U1131611.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5960123.9%
 
t3084912.4%
 
e3084912.4%
 
a3084912.4%
 
d3084912.4%
 
y3084912.4%
 
w122184.9%
 
n122184.9%
 
p113164.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin351364100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5960117.0%
 
N4738313.5%
 
S308498.8%
 
t308498.8%
 
e308498.8%
 
a308498.8%
 
d308498.8%
 
y308498.8%
 
D122183.5%
 
w122183.5%
 
n122183.5%
 
U113163.2%
 
p113163.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII351364100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5960117.0%
 
N4738313.5%
 
S308498.8%
 
t308498.8%
 
e308498.8%
 
a308498.8%
 
d308498.8%
 
y308498.8%
 
D122183.5%
 
w122183.5%
 
n122183.5%
 
U113163.2%
 
p113163.2%
 
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101060 
Steady
 
692
Up
 
8
Down
 
6
ValueCountFrequency (%) 
No10106099.3%
 
Steady6920.7%
 
Up8< 0.1%
 
Down6< 0.1%
 
2022-02-02T15:49:10.727874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:10.798774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:10.883687image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.027317572
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10106649.0%
 
N10106049.0%
 
S6920.3%
 
t6920.3%
 
e6920.3%
 
a6920.3%
 
d6920.3%
 
y6920.3%
 
U8< 0.1%
 
p8< 0.1%
 
D6< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10454650.7%
 
Uppercase Letter10176649.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10106099.3%
 
S6920.7%
 
U8< 0.1%
 
D6< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10106696.7%
 
t6920.7%
 
e6920.7%
 
a6920.7%
 
d6920.7%
 
y6920.7%
 
p8< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin206312100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10106649.0%
 
N10106049.0%
 
S6920.3%
 
t6920.3%
 
e6920.3%
 
a6920.3%
 
d6920.3%
 
y6920.3%
 
U8< 0.1%
 
p8< 0.1%
 
D6< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII206312100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10106649.0%
 
N10106049.0%
 
S6920.3%
 
t6920.3%
 
e6920.3%
 
a6920.3%
 
d6920.3%
 
y6920.3%
 
U8< 0.1%
 
p8< 0.1%
 
D6< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101753 
Steady
 
13
ValueCountFrequency (%) 
No101753> 99.9%
 
Steady13< 0.1%
 
2022-02-02T15:49:10.991549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:11.057472image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:11.128389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000510976
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10175350.0%
 
o10175350.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10181850.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101753> 99.9%
 
S13< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10175399.9%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203584100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10175350.0%
 
o10175350.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203584100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10175350.0%
 
o10175350.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1
ValueCountFrequency (%) 
No101765> 99.9%
 
Steady1< 0.1%
 
2022-02-02T15:49:11.237279image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-02T15:49:11.303185image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:11.375113image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177050.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101765> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101765> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203536100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203536100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101764 
Steady
 
2
ValueCountFrequency (%) 
No101764> 99.9%
 
Steady2< 0.1%
 
2022-02-02T15:49:11.483986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:11.549909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:11.620814image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000078612
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176450.0%
 
o10176450.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177450.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101764> 99.9%
 
S2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101764> 99.9%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203540100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176450.0%
 
o10176450.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203540100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176450.0%
 
o10176450.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1
ValueCountFrequency (%) 
No101765> 99.9%
 
Steady1< 0.1%
 
2022-02-02T15:49:11.728704image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-02-02T15:49:11.794623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:11.866527image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177050.0%
 
Uppercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101765> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101765> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203536100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203536100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176550.0%
 
o10176550.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

change
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
54755 
Ch
47011 
ValueCountFrequency (%) 
No5475553.8%
 
Ch4701146.2%
 
2022-02-02T15:49:11.980394image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:12.056305image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:12.119244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N5475526.9%
 
o5475526.9%
 
C4701123.1%
 
h4701123.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10176650.0%
 
Lowercase Letter10176650.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N5475553.8%
 
C4701146.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5475553.8%
 
h4701146.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203532100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N5475526.9%
 
o5475526.9%
 
C4701123.1%
 
h4701123.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203532100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N5475526.9%
 
o5475526.9%
 
C4701123.1%
 
h4701123.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Yes
78363 
No
23403 
ValueCountFrequency (%) 
Yes7836377.0%
 
No2340323.0%
 
2022-02-02T15:49:12.184156image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
NO
54864 
>30
35545 
<30
11357 
ValueCountFrequency (%) 
NO5486453.9%
 
>303554534.9%
 
<301135711.2%
 
2022-02-02T15:49:12.250092image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-02-02T15:49:12.315003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:49:12.387918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.460880844
Min length2

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N5486421.9%
 
O5486421.9%
 
34690218.7%
 
04690218.7%
 
>3554514.2%
 
<113574.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10972843.8%
 
Decimal Number9380437.5%
 
Math Symbol4690218.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N5486450.0%
 
O5486450.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>3554575.8%
 
<1135724.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
34690250.0%
 
04690250.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common14070656.2%
 
Latin10972843.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N5486450.0%
 
O5486450.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
34690233.3%
 
04690233.3%
 
>3554525.3%
 
<113578.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII250434100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N5486421.9%
 
O5486421.9%
 
34690218.7%
 
04690218.7%
 
>3554514.2%
 
<113574.5%
 

Interactions

2022-02-02T15:48:30.723601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:30.942346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.083182image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.220021image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.353866image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.486722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.616558image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.746419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:31.876255image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.008117image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.129958image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.260818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.385676image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.518504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.660338image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.808178image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:32.950999image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.090836image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.232682image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.376518image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.522331image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.665164image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.808996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:33.943838image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:34.089681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:34.228506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:34.369354image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:34.505199image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:34.740924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:34.878746image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.016598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.151444image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.289284image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.425108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.562947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.699799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.826639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:35.961481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.095337image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.232177image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.360034image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.494858image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.626722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.752557image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:36.882422image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.014264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.145098image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.274959image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.406793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.531659image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.663505image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.789359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:37.921192image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.047056image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.179902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.308755image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.434591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.563453image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.694300image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.824136image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:38.953998image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:39.201696image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:39.322571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:39.453401image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:39.584249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:39.728080image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:39.858927image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.000774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.134618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.266468image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.399308image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.535146image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.666983image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.800827image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:40.936668image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.061535image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.194379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.323216image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.456061image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.586908image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.732750image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.867580image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:41.997441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.131289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.266127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.400973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.536798image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.671653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.813475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:42.949316image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.078183image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.213020image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.341874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.481694image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.616549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.746385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:43.878243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.010089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.142934image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.275766image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.410621image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.532466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.812140image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:44.939990image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.073834image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.210686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.355517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.496340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.636189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.775014image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:45.916861image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.058695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.198532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.339367image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.471213image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.613035image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.756868image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:46.900716image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.028550image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.163393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.295239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.423089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.550940image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.679789image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.807640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:47.934503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.060356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.177224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.303072image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.422920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.548773image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.680636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.821471image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:48.958307image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.091152image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.225995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.361824image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.498676image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.634517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.770346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:49.897210image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.033055image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.164897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.302724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.424582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.552445image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.679296image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.800142image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:50.924016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.049851image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.175718image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.302556image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.611195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.727059image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.852913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:51.973783image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.100623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.233468image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.372318image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.510144image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.641991image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.776833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:52.912674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:53.048532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:53.184357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:53.320198image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:53.448049image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:53.584905image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:53.716748image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-02-02T15:49:12.487818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-02T15:49:12.700553image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-02T15:49:12.914315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-02T15:49:13.176995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-02-02T15:49:13.634478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-02-02T15:48:54.700602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-02T15:48:57.928813image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Sample

First rows

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81252248330783CaucasianFemale[80-90)?21413??68228000398427388NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
91573863555939CaucasianFemale[90-100)?33412?InternalMedicine333180004341984868NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO

Last rows

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10176244384778274694222AfricanAmericanFemale[80-90)?1455MC?333180015602767879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
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10176444385716631693671CaucasianFemale[80-90)?23710MCSurgery-General452210019962859989NoneNoneNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
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